CONTEMPORANEOUS AUTOREGRESSIVE-MOVING AVERAGE (CARMA) MODELING IN WATER RESOURCES1

ABSTRACT: The Contemporaneous Autoregressive-Moving Average (CARMA) model is a simple and efficient model that can be used to fit many multivariate hydrological time series. For certain types of multistation river flow systems, the CARMA model is naturally obtained when the physical restrictions of the system or the characteristics of the data are taken in consideration during the formulation of the model. It is shown how the CARMA model can optimally be used to handle multiple time series where the number of observations in each series may be different. Adequate model building techniques, as well as computational and statistical efficient algorithms to estimate the parameters of the model, are given. The methodologies and applications of the CARMA model are illustrated with three examples. It is also shown how the full multivariate ARMA model may lead to losses in efficient of the estimators when the CARMA model is adequate.     

Improving Benefit Transfer Demand Functions: A GIS Approach

Methodologies for transfer of non-market, natural resource recreation benefits are an active research topic. This arises because of the appeal of modelling the impacts of possible changes in site quality or benefits at unsurveyed sites. However, successful benefit transfer must necessarily rely on development of reliable visitor demand functions that incorporate travel time, demographic and substitute factors. Previous efforts to include all of these elements in a single arrivals model are rare. By integrating data from numerous sources within a geographical information system (GIS) we have developed a model to predict the number of visitors to a recreational woodland in eastern England. Variables were classified into discrete groups that were combined into comparatively homogeneous zones from which to calculate visit rates. Poisson regression techniques were then applied in a stepwise procedure to assess the influence of each determinant. Our analysis highlighted both substantial promise and some caveats in using GIS for future benefit transfer work.     

Environmental degradation and remediation: is economics part of the problem?

It is argued that standard environmental economic and 'ecological economics', have the same fundamentals of valuation in terms of money, based on a demand curve derived from utilitymaximization. But this approach leads to three different measuresof value. An invariant measure of value exists only if the consumer has 'homothetic preferences'. In order to obtain a numerical estimate of value, specific functional forms are necessary, but typically these estimates do not converge. This is due to the fact that the underlying economic model is not structurally stable.According to neoclassical economics, any environmental remediation can be justified only in terms of increases in consumer satisfaction, balancing marginal gains against marginal costs. It is not surprising that the optimal policy obtained fromthis approach suggests only small reductions in greenhouse gases.We show that a unidimensional metric of consumer's utility measured in dollar terms can only trivialize the problem of global climate change.     

Testing the MAGIC acid rain model in highly organic,low-conductivity waters using multiple calibrations

Accurate predictions of acid precipitation effects on water resources are important in order to allow a better understanding of various pollution control strategy outcomes. Dynamic geochemical models have been developed to address this need, but have to be tested under a variety of environmental conditions to provide confidence in their predictions. The most commonly used aquatic acidification model in North America and Europe is the model of acidification of groundwater in catchments (MAGIC). Though extensively used, MAGIC has never been tested in catchments with extremely low ionic strength water and high in natural organic acids (NOAs) from wetlands, two conditions which are common in large parts of Canada. We calibrated the model for two catchments located in Nova Scotia, Canada, which had some of the most dilute freshwaters reported in the literature and very high NOA. We also evaluated the variability inherent in calibration data sets by using five separate data sets collected over a 15-year period at the same sites. We show good model simulations for the main cations and anions in catchment waters. However, modeling pH is more difficult in the highly organic waters and requires modification to the acid dissociation constants. Calculated acid neutralization capacity can also be more difficult to model due to the low ion content making small errors more important. In theory, multiple calibrations of a model at a same site should produce identical hindcasts and predictions. In reality, the multiple calibrations produced a series of similar, but not identical outcomes which give a probable range of past values and future outcomes. We feel that this practical approach to validation is a useful addition to the arsenal of model testing tools.     

Mathematical Methods for Spatially Cohesive Reserve Design

The problem of designing spatially cohesive nature reserve systems that meet biodiversity objectives is formulated as a nonlinear integer programming problem. The multiobjective function minimises a combination of boundary length, area and failed representation of the biological attributes we are trying to conserve. The task is to reserve a subset of sites that best meet this objective. We use data on the distribution of habitats in the Northern Territory, Australia, to show how simulated annealing and a greedy heuristic algorithm can be used to generate good solutions to such large reserve design problems, and to compare the effectiveness of these methods.     

Airborne particulate-associated polyaromatic hydrocarbons,n-alkanes,elemental and organic carbon in three European cities

Total suspended particulate (TSP) samples were collected weekly over a period of one year at four European sites during 1995/6. Two sites were in London-a Central London site (CL, St Paul's Cathedral) and a suburban North London site (NL, Bounds Green); the other two sites were in Porto, Portugal and Vienna, Austria. TSP was collected using a low volume sampler. Organic carbon (OC) and elemental carbon (EC) concentrations were measured using a thermal-optical carbon analyser. Parallel samplers collected TSP for subsequent GC-MS analysis of thirty-nine combustion-associated organic compounds; 16 polyaromatic hydrocarbons (PAHs) and 23 n-alkanes. OC and EC correlate well at all sites (r2 = 0.39-0.65), although the London inter-site correlations were low, suggesting that local sources of OC and EC have a significant influence on local concentrations. Concentrations do not vary widely across the four urban sites, despite the significant differences in urban characteristics. Seasonal patterns of OC:EC ratios were similar at the London and Vienna sites, with highest ratios in autumn and winter, and annual mean OC:EC ratios were identical at these sites. The Carbon Preference Index (CPI) indicated vehicle emissions to have a stronger influence over particulate concentrations at the Vienna and central London sites; there was a stronger biogenic signature in north London and Porto. In addition, two PAH compounds (pyrene and fluoranthene) previously associated with diesel exhaust, were correlated with OC and EC concentrations at the London and Vienna sites.